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--- |
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license: mit |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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- gopipasala/fka-awesome-chatgpt-prompts |
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metrics: |
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- character |
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base_model: |
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- meta-llama/Llama-3.2-11B-Vision-Instruct |
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new_version: meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: summarization |
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library_name: diffusers |
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tags: |
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- legal |
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--- |
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Model Card for Rithu Paran's Summarization Model |
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Model Details |
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Model Description |
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Purpose: This model is designed for text summarization, specifically built to condense long-form content into concise, meaningful summaries. |
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Developed by: Rithu Paran |
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Model type: Transformer-based Language Model for Summarization |
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Base Model: Meta-Llama/Llama-3.2-11B-Vision-Instruct |
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Finetuned Model Version: Meta-Llama/Llama-3.1-8B-Instruct |
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Language(s): Primarily English, with limited support for other languages. |
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License: MIT License |
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Model Sources |
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Repository: Available on Hugging Face Hub under Rithu Paran |
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Datasets Used: fka/awesome-chatgpt-prompts, gopipasala/fka-awesome-chatgpt-prompts |
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Uses |
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Direct Use |
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This model can be directly employed for summarizing various types of content, such as news articles, reports, and other informational documents. |
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Out-of-Scope Use |
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It is not recommended for highly technical or specialized documents without additional fine-tuning or adaptation. |
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Bias, Risks, and Limitations |
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While this model was designed to be general-purpose, there may be inherent biases due to the training data. Users should be cautious when using the model for sensitive content or in applications where accuracy is crucial. |
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How to Get Started with the Model |
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Here's a quick example of how to start using the model for summarization: |
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python |
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Copy code |
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from transformers import pipeline |
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summarizer = pipeline("summarization", model="rithu-paran/your-summarization-model") |
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text = "Insert long-form text here." |
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summary = summarizer(text, max_length=100, min_length=30) |
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print(summary) |
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Training Details |
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Training Data |
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Datasets: fka/awesome-chatgpt-prompts, gopipasala/fka-awesome-chatgpt-prompts |
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Preprocessing: Data was tokenized and normalized for better model performance. |
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Training Procedure |
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Hardware: Trained on GPUs with Hugging Face API resources. |
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Precision: Mixed-precision (fp16) was utilized to enhance training efficiency. |
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Training Hyperparameters |
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Batch Size: 16 |
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Learning Rate: 5e-5 |
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Epochs: 3 |
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Optimizer: AdamW |
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Evaluation |
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Metrics |
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Metrics Used: ROUGE Score, BLEU Score |
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Evaluation Datasets: Evaluated on a subset of fka/awesome-chatgpt-prompts for summarization performance. |
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Technical Specifications |
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Model Architecture |
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Based on Llama-3 architecture, optimized for summarization through attention-based mechanisms. |
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Compute Infrastructure |
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Hardware: Nvidia A100 GPUs were used for training. |
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Software: Hugging Face’s transformers library along with the diffusers library. |
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Environmental Impact |
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Hardware Type: Nvidia A100 GPUs |
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Training Duration: ~10 hours |
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Estimated Carbon Emission: Approximate emissions calculated using Machine Learning Impact calculator. |
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Contact |
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For any questions or issues, please reach out to Rithu Paran via the Hugging Face Forum. |
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